CN111398980B - Method and device for processing airborne LiDAR data - Google Patents
Method and device for processing airborne LiDAR data Download PDFInfo
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- CN111398980B CN111398980B CN201811631910.4A CN201811631910A CN111398980B CN 111398980 B CN111398980 B CN 111398980B CN 201811631910 A CN201811631910 A CN 201811631910A CN 111398980 B CN111398980 B CN 111398980B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
- G01S17/42—Simultaneous measurement of distance and other co-ordinates
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/87—Combinations of systems using electromagnetic waves other than radio waves
- G01S17/875—Combinations of systems using electromagnetic waves other than radio waves for determining attitude
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a method and a device for processing airborne LiDAR data, which relate to the technical field of unmanned aerial vehicle mapping, and the method comprises the following steps: generating optimal smooth track data by processing Global Positioning System (GPS) data and inertial navigation data, and simultaneously processing laser detection and measurement LiDAR data to obtain three-dimensional coordinate data under a LiDAR coordinate system; performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system to obtain first optimal smooth track data and first three-dimensional coordinate data; obtaining second optimal smooth track data by carrying out interpolation processing on the first optimal smooth track data; and performing space synchronization processing by using the second optimal smooth track data and the first three-dimensional coordinate data to obtain and output the space synchronization result.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle mapping, in particular to a method and a device for processing airborne LiDAR data.
Background
With the advancement of technology and the increasing demand of people for geospatial information, geospatial information science, which is composed of modern information technologies such as GPS (Global Positioning System ), RS (Remote Sensing technology), GIS (Geographic Information System ) and the internet, is being continuously perfected and developed. The current technical means for spatial information acquisition are also more and more diversified, and progress is being made towards multiple sensors, multiple angles, multiple polarizations, high time resolution, high spatial resolution, high spectral resolution and the like. As a novel earth observation technology, airborne LiDAR (Light Detection And Ranging, laser detection and measurement) has attracted wide attention in various industries due to the advantages of low initiative, little influence by weather, no influence by shadows, certain penetrability to gaps between ground features and the like. The airborne LiDAR integrates a plurality of advanced technologies such as GPS technology, INS inertial navigation technology, laser ranging technology and the like, can simultaneously obtain three-dimensional coordinates, reflection intensity and ground image information of a large number of dense ground points, and becomes an extremely important earth space information acquisition mode gradually. Unlike microwaves and traditional optical imaging, most LiDAR sensors use infrared or near infrared wavelengths to directly obtain three-dimensional spatial coordinates of ground points. The technology can directly collect three-dimensional data of various entities or real scenes, and quickly reconstruct a three-dimensional model of a target, thereby obtaining various drawing data of lines, surfaces, bodies and the like of a three-dimensional space. At present, the airborne LiDAR is gradually applied to a plurality of fields such as three-dimensional city modeling, large-scale high-precision digital ground model acquisition, high-precision real shot image production, forest resource management and evaluation, pipeline investigation and the like.
The airborne LiDAR relates to a series of problems such as multi-sensor data reading and fusion, time alignment, coordinate system conversion, sensor calibration and the like, and is a difficult task for realizing mapping-level precision.
Disclosure of Invention
The technical problem of the point cloud LAS data after the reconstruction from the multi-sensor original data is solved by the scheme provided by the embodiment of the invention.
The method for processing the airborne LiDAR data provided by the embodiment of the invention comprises the following steps:
generating optimal smooth track data by processing Global Positioning System (GPS) data and inertial navigation data, and simultaneously processing laser detection and measurement LiDAR data to obtain three-dimensional coordinate data under a LiDAR coordinate system;
performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system to obtain first optimal smooth track data and first three-dimensional coordinate data;
obtaining second optimal smooth track data by carrying out interpolation processing on the first optimal smooth track data;
and performing space synchronization processing by using the second optimal smooth track data and the first three-dimensional coordinate data to obtain and output the space synchronization result.
Preferably, acquiring the GPS data containing position time information and position information by means of a GNSS (Global Navigation Satellite System ) in an acquisition mode of a first frequency; acquiring the inertial navigation data comprising attitude time information and attitude information in a second frequency acquisition mode through an INS (Inertial Navigation System) and an inertial navigation system; acquiring the LiDAR data containing coordinate time information and coordinate information in a third frequency acquisition mode through LiDAR equipment;
wherein the first frequency < the second frequency < the third frequency.
Preferably, the generating the optimal smooth trajectory data by processing global positioning system GPS data and inertial navigation data comprises:
and (3) performing complementation and deviation correction processing on the position information in the GPS data and the attitude information in the inertial navigation data respectively by adopting a PPK (Post Processed Kinematic, dynamic post-processing technology) to generate optimal smooth track data.
Preferably, the obtaining the first optimal smooth track data and the first three-dimensional coordinate data by performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system includes:
the optimal smooth trajectory data acquisition UTC (Coordinated Universal Time ) time;
and carrying out time synchronization processing on the local time of the three-dimensional coordinate data under the LiDAR coordinate system and the coordinated universal time UTC time to obtain first optimal smooth track data and first three-dimensional coordinate data.
Preferably, the obtaining the second best smooth track data by performing interpolation processing on the first best smooth track data includes:
performing interpolation processing on the first optimal smooth track data by using an interpolation method to obtain second optimal smooth track data;
wherein the interpolation method includes any one of: nearest neighbor interpolation method, linear interpolation method, bilinear quadratic interpolation method, and cubic spline interpolation method.
Preferably, after obtaining the second best smoothed trajectory data, further comprising:
and sampling interpolation data in the second optimal smooth track data according to the first three-dimensional coordinate data, so that each first three-dimensional coordinate data is matched with the second optimal smooth track data in a one-to-one correspondence manner.
Preferably, the performing spatial synchronization processing using the second optimal smooth trajectory data and the first three-dimensional coordinate data, and obtaining and outputting the spatial synchronization result includes:
according to the first three-dimensional coordinate data, three-dimensional coordinate information of the object to be detected under the LiDAR coordinate system is obtained;
carrying out space synchronization processing on the three-dimensional coordinate information of the object to be detected from the LiDAR coordinate system by utilizing the second optimal smooth track data and the first three-dimensional coordinate data to obtain the three-dimensional coordinate information of the object to be detected under the geodetic coordinate;
and storing the three-dimensional coordinate information of the object to be tested under the geodetic coordinates in the form of an LAS file, and outputting the stored LAS file when receiving an output instruction.
Preferably, the spatial synchronization processing of the three-dimensional coordinate information of the object to be measured from the LiDAR coordinate system includes:
down-converting from the LiDAR coordinate system to an Inertial Measurement Unit (IMU) coordinate system;
down-converting from the IMU coordinate system to a northeast NED coordinate system;
down-converting from the NED coordinate system to a geocentric geodetic ECEF coordinate system;
down-converting from the ECEF coordinate system to a universal cross ink card grid system UTM coordinate system.
According to an embodiment of the invention, an apparatus for processing airborne LiDAR data includes:
the data acquisition module is used for generating optimal smooth track data by processing Global Positioning System (GPS) data and inertial navigation data, and processing laser detection and measurement LiDAR data to obtain three-dimensional coordinate data under a LiDAR coordinate system;
the time synchronization module is used for obtaining first optimal smooth track data and first three-dimensional coordinate data by performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system;
the interpolation module is used for obtaining second optimal smooth track data by carrying out interpolation processing on the first optimal smooth track data;
and the space synchronization module is used for carrying out space synchronization processing by utilizing the second optimal smooth track data and the first three-dimensional coordinate data to obtain and output the space synchronization result.
Preferably, acquiring the GPS data containing position time information and position information in a first frequency acquisition mode through a Global Navigation Satellite System (GNSS); acquiring inertial navigation data comprising attitude time information and attitude information in a second frequency acquisition mode through an inertial navigation system INS; acquiring the LiDAR data containing coordinate time information and coordinate information in a third frequency acquisition mode through LiDAR equipment;
wherein the first frequency < the second frequency < the third frequency.
According to the scheme provided by the embodiment of the invention, from data acquisition, analysis, calculation, time and space synchronization of multiple sensors and the like, a whole set of process algorithms are realized, and the efficient reconstruction of environment information in the mapping field of the unmanned aerial vehicle-mounted LiDAR is realized.
Drawings
FIG. 1 is a flowchart of a method for processing airborne LiDAR data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an apparatus for processing airborne LiDAR data according to an embodiment of the present invention;
FIG. 3 is a flow chart of an overall algorithm provided by an embodiment of the present invention;
FIG. 4 is a graph of the results of an interpolation algorithm employed in an embodiment of the present invention;
FIG. 5 is an overall flow chart of coordinate system conversion by the overall spatial synchronization provided in an embodiment of the present invention;
FIG. 6 is a diagram showing the final result of the algorithm in an embodiment of the present invention.
Detailed Description
The following detailed description of the preferred embodiments of the present invention is provided in conjunction with the accompanying drawings, and it is to be understood that the preferred embodiments described below are merely illustrative and explanatory of the invention, and are not restrictive of the invention.
FIG. 1 is a flowchart of a method for processing airborne LiDAR data according to an embodiment of the present invention, as shown in FIG. 1, including:
step S101: generating optimal smooth track data by processing Global Positioning System (GPS) data and inertial navigation data, and simultaneously processing laser detection and measurement LiDAR data to obtain three-dimensional coordinate data under a LiDAR coordinate system;
step S102: performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system to obtain first optimal smooth track data and first three-dimensional coordinate data;
step S103: obtaining second optimal smooth track data by carrying out interpolation processing on the first optimal smooth track data;
step S104: and performing space synchronization processing by using the second optimal smooth track data and the first three-dimensional coordinate data to obtain and output the space synchronization result.
Acquiring the GPS data containing position time information and position information in a first frequency acquisition mode through a Global Navigation Satellite System (GNSS); acquiring inertial navigation data containing attitude time information and attitude information in a second frequency acquisition mode through an inertial navigation system; acquiring the LiDAR data containing coordinate time information and coordinate information in a third frequency acquisition mode through LiDAR equipment; wherein the first frequency < the second frequency < the third frequency.
Specifically, the generating the optimal smooth trajectory data by processing global positioning system GPS data and inertial navigation data includes: and respectively carrying out complementation and deviation correction processing on the position information in the GPS data and the attitude information in the inertial navigation data by adopting a dynamic post-processing technology PPK to generate optimal smooth track data.
Specifically, the obtaining the first optimal smooth track data and the first three-dimensional coordinate data by performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system includes: the optimal smooth track data is obtained to coordinate the universal time UTC time; and carrying out time synchronization processing on the local time of the three-dimensional coordinate data under the LiDAR coordinate system and the coordinated universal time UTC time to obtain first optimal smooth track data and first three-dimensional coordinate data.
Specifically, the obtaining the second optimal smooth track data by performing interpolation processing on the first optimal smooth track data includes: performing interpolation processing on the first optimal smooth track data by using an interpolation method to obtain second optimal smooth track data; wherein the interpolation method includes any one of: nearest neighbor interpolation method, linear interpolation method, bilinear quadratic interpolation method, and cubic spline interpolation method.
After obtaining the second best smooth track data, the embodiment of the invention further comprises: and sampling interpolation data in the second optimal smooth track data according to the first three-dimensional coordinate data, so that each first three-dimensional coordinate data is matched with the second optimal smooth track data in a one-to-one correspondence manner.
Specifically, the performing spatial synchronization processing using the second optimal smooth trajectory data and the first three-dimensional coordinate data, and obtaining and outputting the spatial synchronization result includes: according to the first three-dimensional coordinate data, three-dimensional coordinate information of the object to be detected under the LiDAR coordinate system is obtained; carrying out space synchronization processing on the three-dimensional coordinate information of the object to be detected from the LiDAR coordinate system by utilizing the second optimal smooth track data and the first three-dimensional coordinate data to obtain the three-dimensional coordinate information of the object to be detected under the geodetic coordinate; and storing the three-dimensional coordinate information of the object to be tested under the geodetic coordinates in the form of an LAS file, and outputting the stored LAS file when receiving an output instruction.
Specifically, the spatial synchronization processing of the three-dimensional coordinate information of the object to be measured from the LiDAR coordinate system includes: down-converting from the LiDAR coordinate system to an Inertial Measurement Unit (IMU) coordinate system; down-converting from the IMU coordinate system to a northeast NED coordinate system; down-converting from the NED coordinate system to a geocentric geodetic ECEF coordinate system; down-converting from the ECEF coordinate system to a universal cross ink card grid system UTM coordinate system.
Fig. 2 is a schematic diagram of an apparatus for processing airborne LiDAR data according to an embodiment of the present invention, where, as shown in fig. 2, the apparatus includes: a data acquisition module 201, a time synchronization module 202, an interpolation module 203, and a spatial synchronization module 204.
The data acquisition module 201 is configured to generate optimal smooth track data by processing GPS data and inertial navigation data, and process laser detection and measurement LiDAR data to obtain three-dimensional coordinate data in a LiDAR coordinate system; the time synchronization module 202 is configured to obtain first optimal smooth track data and first three-dimensional coordinate data by performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system; the interpolation module 203 is configured to obtain second best smooth track data by performing interpolation processing on the first best smooth track data; the spatial synchronization module 204 is configured to perform spatial synchronization processing using the second optimal smooth trajectory data and the first three-dimensional coordinate data, so as to obtain and output the spatial synchronization result.
Acquiring the GPS data containing position time information and position information in a first frequency acquisition mode through a Global Navigation Satellite System (GNSS); acquiring inertial navigation data containing attitude time information and attitude information in a second frequency acquisition mode through an inertial navigation system; acquiring the LiDAR data containing coordinate time information and coordinate information in a third frequency acquisition mode through LiDAR equipment; wherein the first frequency < the second frequency < the third frequency.
FIG. 3 is a flowchart of an overall algorithm provided by an embodiment of the present invention, as shown in FIG. 3, including:
step 301: each sensor data acquisition and decoding are used for generating an optimal smooth track and acquiring coordinate information of LiDAR data;
firstly, acquiring original GPS data (the current position under an earth coordinate system) and inertial navigation data (navigation parameters such as carrier gesture) and performing calculation by adopting a Kalman filtering algorithm in a tight coupling mode to generate an optimal smooth track, acquiring LiDAR original data by reading network port data flow, analyzing the data structure of the original data, performing calculation and interpolation, and obtaining three-dimensional information under the LiDAR coordinate system. That is, the hardware device has a GNSS/INS integrated navigation positioning module, the satellite positioning navigation system obtains the current position under the earth coordinate system, while the inertial directional positioning navigation system provides navigation parameters such as carrier gesture through the internal inertial device (gyro, accelerometer), records the original data of the base station and the mobile station in the integrated positioning navigation system, and adopts PPK technology to complement the two data, correct the deviation, and calculate and synthesize SBET (Smoothed BestEstimate Trajectory, best smooth track) data. In the aspect of LiDAR sensor data, original data are captured through network ports, the data are divided into data packets and positioning packets, the data packets comprise distance, angle and time stamp information, three-dimensional coordinate information and accurate time stamps can be restored through resolving and interpolation, GPRMC sentences sent to LiDAR by an inertial navigation module through PPS pulse signals are recorded in the positioning packets, and time information and position information are recorded.
Wherein, each sensor refers to GNSS, INS and LiDAR equipment.
Step 302: time synchronization alignment for time unification and calibration of multiple sensors;
and the LiDAR device is connected with the LiDAR device through inertial navigation, GPRMC statement is output, the LiDAR device captures PPS pulse signals, the GPRMC statement provides UTC time provided by the inertial navigation device, the LiDAR device records local time at any time, the two times are stored in a locating packet of LiDAR data, and the local time and the UTC time provided by the PPS signals can be aligned and synchronized through resolving the locating packet data. That is, the data collected by the LiDAR equipment and the data output by inertial navigation can be synchronously aligned one by one in time. The time alignment problem in multi-sensor data fusion is one of the key issues to be addressed by data fusion systems. To ensure the accuracy of the coordinate conversion of the multisensor data, the data must be time aligned before fusion of the data can take place. Otherwise, misaligned data may result in poorer fusion performance than when using one of the sensor data alone. The GPS receiver generates PPS (Pulse Per Second) Pulse signal and NMEA $GPRMC message, the GPRMC message provides UTC time, and the LiDAR sensor sets its own time stamp to microseconds of which each UTC time exceeds one hour, and by analyzing two corresponding times of the GPRMC statement, each data point in the LiDAR data packet corresponds to the last UTC time, so that alignment with SBET (smooth best estimate path) data can be performed.
Step 303: data interpolation for alignment of different frequencies of the multi-sensor data;
and the data interpolation step is to interpolate the data in the optimal smooth track by adopting a cubic spline interpolation algorithm, the interpolated data is changed into infinite continuous data from the prior finite discrete point data, and then the frequency of the corresponding LiDAR data is used for sampling the interpolated data so as to obtain unification and correspondence in frequency. That is, since the frequencies of the sensor data are different, the frequency of the inertial navigation data is 200HZ at the maximum, the data sampling frequency of the GNSS (global navigation satellite system) is 100HZ at the maximum, and the frequency of the LiDAR data may exceed 200KHZ, so in order to obtain the position and posture information of each LiDAR, interpolation of the data is required for the SBET result of the combined GNSS/IMU navigation output.
There are several methods of interpolation, nearest interpolation, linear interpolation, bilinear quadratic interpolation, cubic spline interpolation. The algorithm of the embodiment of the invention adopts cubic spline interpolation.
The cubic spline interpolation (Cubic Spline Interpolation) essentially belongs to a piecewise polynomial smooth interpolation, the basic idea being that a low order polynomial is approximated within each cell made up of two adjacent nodes,and the junction at each junction is guaranteed to be smooth (i.e., derivative continuous). Suppose that in [ a, b ]]There are n+1 data in the interval, assuming at t i The data at time is f (t) =y i Then there is a cubic spline interpolation function s (x) that satisfies the following condition:
(1)s(t i )＝y i ；
(2) s (t) in each inter-cell [ t ] i ,t i+1 ]Is a cubic polynomial;
(3) s (t) has a second continuous derivative over [ a, b ].
The formula of the cubic spline interpolation function s (t) can be obtained by the least square and Hermite interpolation formula:
wherein m is i ＝s'(t i )。
The finishing method can obtain: in interval t i ≤t≤t i+1 Among these are the following equations:
s(t)＝a i +b i (x-x i )+c i (x-x i ) 2 +d i (x-x i ) 3
wherein:
h i ＝t i+1 -t i
a i ＝y i
after the cubic spline interpolation fitting, a smooth curve can be obtained, a limited point is changed into an infinite point, an effective value is available at any time in the interval range of the curve, the curve is sampled and aligned according to the time of the synchronized LiDAR data, and therefore each LiDAR data also has one-to-one corresponding track data, and fusion alignment can be carried out.
As shown in fig. 4, there are position and attitude angle data, wherein the straight broken line part in each data is the original data, the data is not subjected to cubic spline interpolation, and the smooth curve part is the data subjected to cubic spline interpolation, so that obvious improvement can be seen.
Step 304: the space synchronization is used for converting a coordinate system and restoring to a geodetic coordinate system;
the step of space synchronization is to perform a plurality of coordinate system conversions, convert the coordinate system from a relative LiDAR coordinate system to an inertial navigation coordinate system, then convert the coordinate system to a local north east ground coordinate system, then convert the coordinate system to a geocentric earth fixed (ECEF) coordinate system, then convert the coordinate system to a WGS-84 coordinate system, and finally project the coordinate system to a UTM geodetic coordinate system, thereby realizing the accurate reconstruction of the absolute position required by mapping.
Fig. 5 is an overall flowchart of coordinate system conversion performed by the whole spatial synchronization provided in the embodiment of the present invention, as shown in fig. 5, to perform multiple conversions, from a point to be measured to a LiDAR coordinate system, to an IMU (Inertial measurement unit ) coordinate system, to a local northeast coordinate system, to an ECEF (Earth-Centered, earth-Fixed) coordinate system of the Earth, and further to a WGS84 (World Geodetic System-1984 Coordinate System) coordinate system, and finally mapped to a UTM (universal transverse mercartorgrid system, universal transverse ink-card grid system) coordinate system.
Step 501: the LiDAR coordinate system obtains the three-dimensional coordinate of the to-be-measured point by resolving LiDAR data.
Step 502: conversion from LiDAR to IMU, i.e. inertial navigation, assuming LiDAR to be S, IMU to be b, three angles of placement of LiDAR to IMU to be pitch angle θ x Roll angle theta y Bias ofAngle of flight theta z Three lever arm values a b Is [ a ] L ,b L ,c L ] T ，
For LiDAR to IMU rotation matrix, +.>
The method comprises the following steps:
let r i S ＝[X L Y L Z L ] T For LiDAR position coordinates, r i For coordinate position to IMU, conversion from LiDAR to IMU is
Step 503, from IMU to local NED coordinate system, NED coordinate system is also called north east ground coordinate system, defining each axis of NED coordinate system: n-the north axis points to earth north; e-the east axis points to the east of the earth; d-the earth axis is perpendicular to the earth's surface and pointing downward. Assuming that the central coordinate system of the IMU is a b system, the local NED coordinate system is an l system, and the IMU is converted into an attitude angle of rotation of the l system around respective axes: roll angle roll about X-axis, pitch about Y-axis, yaw about Z-axis, let
The rotation transformation matrix from the IMU to the NED coordinate system is as follows:
r i to the coordinate position of the IMU, then to the NED coordinate system is converted into:
wherein:
step 504, from the local NED coordinate system to the geocentric ECEF coordinate system. The Earth-Centered Earth-Fixed (ECEF) coordinate system is abbreviated as Earth-Centered coordinate system, which is a kind of Earth-Centered Earth-Fixed coordinate system (also called Earth coordinate system) with Earth center as origin, and is a kind of cartesian coordinate system. The origin O (0, 0) is the earth centroid, the z axis and the ground axis are parallel to point to the north pole, the x axis points to the intersection point of the original meridian and the equator, and the y axis is perpendicular to the xOz plane (namely, the intersection point of the east 90 degrees and the equator) to form a right-hand coordinate system.
Assuming the ECEF system is an m system, the IMU itself is located at the world coordinate position of ECEF
Let->
For the rotational transformation matrix from NED coordinate system to world coordinate, there is the following relation, in which long l Longitude, lat l The latitude is:
let r i m ＝[x e y e z e ] T For the transformation of the target point position into the ECEF coordinate system, there are:
step 505 there are two small steps from ECEF to UTM, the first being from ECEF to WGS 84. The WGS-84 coordinate system has its origin at the earth centroid, the Z axis pointing in the agreed earth polar (CTP) direction defined by BIH1984.0, the X axis pointing at the intersection of the zero-degree meridian of BIH1984.0 and the CTP equator, the Y and Z, X axes constituting the right hand coordinate system. It is a geodetic (geodetic) coordinate system. The conversion formula is as follows:
wherein (x) e ,y e ,z e ) T Is the x, y and z coordinates of ECEF coordinate system, R N : normal radius, h: ellipsoid height, h=n+h, H: height above level, N: the height of the earth center to the ground level.
lat l : latitude, long l : longitude, e: the exact value of the vector,
a＝6,378,137.0m b＝6,356,752.3142m
the coordinate position of WGS-84 can be obtained: (Long) l ,lat l ,H)
After the WGS84 coordinate system is obtained, it is mapped to the UTM coordinate system. UTM coordinates are planar rectangular coordinates, and such coordinate grid systems and projections upon which they are based have been widely used in topography as reference grids for satellite images and natural resource databases and other applications requiring accurate positioning. In the UTM system, the earth's surface area between 84 degrees north latitude and 80 degrees south latitude is divided into north-south longitudinal bands (projected bands) by 6 degrees longitude. These projections were numbered from 1 to 60 (Beijing at zone 50) starting from the 180 degree meridian and going east. Each band is subdivided into quadrilaterals with a weft differential of 8 degrees. The transverse rows of the quadrangle start from 80 degrees from the south latitude. Each quadrilateral is marked with a combination of numbers and letters in turn, marked with letters C through X (no I and O), the X-th row including the entire land area of the northern hemisphere from 72 degrees to 84 degrees north latitude, 12 degrees total. The reference grid reads right up. Each quadrilateral is divided into a plurality of cells with sides of 1000000 meters, and marked by an letter combination system. In each casting belt, a meridian line located at the center of the belt is given an abscissa value of 500000 meters. The coordinate value of the mark for the equator of the northern hemisphere is 0, and the coordinate value of the mark for the southern hemisphere is 10000000 meters, and the coordinate value of the mark decreases toward the south.
Let (x, y) be the transformed utm coordinates, φ: latitude lambda: longitude. First, a Zone of UTM projection is found:
Zone＝(λ+180)/6+1
λ 0 ＝6.0*Zone-183.0
and then, starting to calculate conversion:
k 0 ＝0.9996
T＝tan 2 φ
C＝e' 2 cos 2 φ
A＝(λ-λ 0 )cosφ
the position (x, y, H) in the UTM coordinate system can be finally obtained through the conversion of the above equation.
Step 505: and outputting the LAS file. The LAS file adopts an industry standard binary format for storing airborne lidar data. After a series of calculation, interpolation and synchronization, each point detected by LiDAR can be attached with a coordinate on a real geodetic coordinate system, and then output into an LAS file.
Fig. 6 is a diagram showing a final result obtained by an algorithm in the embodiment of the present invention, and as shown in fig. 6, final LAS data obtained by adopting the above-mentioned series of processing methods is data of a certain area collected by using an unmanned aerial vehicle mounting device, and a screenshot is checked by QTreader software, so that models of houses, pavements and vehicles can be clearly seen.
According to the scheme provided by the embodiment of the invention, the data is acquired through the airborne LiDAR and the integrated navigation equipment, the calculation and the analysis of the sensor data are performed, the time alignment synchronization and the deviation correction of the multi-sensor data are performed, the interpolation and the sampling of different source data are performed, the algorithm flow of the space conversion of a plurality of coordinate systems is performed, the originally acquired data are reconstructed into a high-precision model structure with absolute geographic coordinates, the requirements of the mapping industry and the subsequent DSM and DEM modeling analysis can be met, and the problem from the multi-sensor original data to the reconstructed point cloud LAS data is solved.
Although the present invention has been described in detail hereinabove, the present invention is not limited thereto and various modifications may be made by those skilled in the art in accordance with the principles of the present invention. Therefore, all modifications made in accordance with the principles of the present invention should be understood as falling within the scope of the present invention.
Claims (8)
1. A method of airborne LiDAR data processing, comprising:
generating optimal smooth track data by processing Global Positioning System (GPS) data and inertial navigation data, and simultaneously processing laser detection and measurement LiDAR data to obtain three-dimensional coordinate data under a LiDAR coordinate system;
performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system to obtain first optimal smooth track data and first three-dimensional coordinate data;
obtaining second optimal smooth track data by carrying out interpolation processing on the first optimal smooth track data, and carrying out sampling processing on interpolation data in the second optimal smooth track data according to the first three-dimensional coordinate data so that each first three-dimensional coordinate data is matched with the second optimal smooth track data in a one-to-one correspondence manner;
and performing spatial synchronization processing by using the second optimal smooth track data and the first three-dimensional coordinate data to obtain and output a spatial synchronization result, wherein the spatial synchronization processing comprises the following steps: according to the first three-dimensional coordinate data, three-dimensional coordinate information of the object to be detected under the LiDAR coordinate system is obtained; carrying out space synchronization processing on the three-dimensional coordinate information of the object to be detected from the LiDAR coordinate system by utilizing the second optimal smooth track data and the first three-dimensional coordinate data to obtain the three-dimensional coordinate information of the object to be detected under the geodetic coordinate; and storing the three-dimensional coordinate information of the object to be tested under the geodetic coordinates in the form of an LAS file, and outputting the stored LAS file when receiving an output instruction.
2. The method according to claim 1, wherein the GPS data comprising position time information and position information is acquired by means of a global navigation satellite system GNSS in an acquisition mode of a first frequency; acquiring inertial navigation data comprising attitude time information and attitude information in a second frequency acquisition mode through an inertial navigation system INS; acquiring the LiDAR data containing coordinate time information and coordinate information in a third frequency acquisition mode through LiDAR equipment;
wherein the first frequency < the second frequency < the third frequency.
3. The method of claim 1, wherein generating optimal smoothed trajectory data by processing global positioning system, GPS, data and inertial navigation data comprises:
and respectively carrying out complementation and deviation correction processing on the position information in the GPS data and the attitude information in the inertial navigation data by adopting a dynamic post-processing technology PPK to generate optimal smooth track data.
4. The method of claim 3, wherein the obtaining the first optimal smoothed trajectory data and the first three-dimensional coordinate data by performing time synchronization processing on the optimal smoothed trajectory data and the three-dimensional coordinate data in the LiDAR coordinate system comprises:
the optimal smooth track data is obtained to coordinate the universal time UTC time;
and carrying out time synchronization processing on the local time of the three-dimensional coordinate data under the LiDAR coordinate system and the coordinated universal time UTC time to obtain first optimal smooth track data and first three-dimensional coordinate data.
5. The method of claim 4, wherein the obtaining second best smoothed trajectory data by interpolating the first best smoothed trajectory data comprises:
performing interpolation processing on the first optimal smooth track data by using an interpolation method to obtain second optimal smooth track data;
wherein the interpolation method includes any one of: nearest neighbor interpolation method, linear interpolation method, bilinear quadratic interpolation method, and cubic spline interpolation method.
6. The method according to claim 1, wherein spatially synchronizing the three-dimensional coordinate information of the object to be measured from the LiDAR coordinate system comprises:
down-converting from the LiDAR coordinate system to an Inertial Measurement Unit (IMU) coordinate system;
down-converting from the IMU coordinate system to a northeast NED coordinate system;
down-converting from the NED coordinate system to a geocentric geodetic ECEF coordinate system;
down-converting from the ECEF coordinate system to a universal cross ink card grid system UTM coordinate system.
7. An apparatus for processing airborne LiDAR data, comprising:
the data acquisition module is used for generating optimal smooth track data by processing Global Positioning System (GPS) data and inertial navigation data, and processing laser detection and measurement LiDAR data to obtain three-dimensional coordinate data under a LiDAR coordinate system;
the time synchronization module is used for obtaining first optimal smooth track data and first three-dimensional coordinate data by performing time synchronization processing on the optimal smooth track data and the three-dimensional coordinate data under the LiDAR coordinate system;
the interpolation module is used for obtaining second optimal smooth track data by carrying out interpolation processing on the first optimal smooth track data, and carrying out sampling processing on interpolation data in the second optimal smooth track data according to the first three-dimensional coordinate data so that each first three-dimensional coordinate data is matched with the second optimal smooth track data in a one-to-one correspondence manner;
the spatial synchronization module is configured to perform spatial synchronization processing by using the second optimal smooth trajectory data and the first three-dimensional coordinate data, and obtain and output a spatial synchronization result, and includes: according to the first three-dimensional coordinate data, three-dimensional coordinate information of the object to be detected under the LiDAR coordinate system is obtained; carrying out space synchronization processing on the three-dimensional coordinate information of the object to be detected from the LiDAR coordinate system by utilizing the second optimal smooth track data and the first three-dimensional coordinate data to obtain the three-dimensional coordinate information of the object to be detected under the geodetic coordinate; and storing the three-dimensional coordinate information of the object to be tested under the geodetic coordinates in the form of an LAS file, and outputting the stored LAS file when receiving an output instruction.
8. The apparatus of claim 7, wherein the GPS data including location time information and location information is acquired by a global navigation satellite system GNSS at a first frequency; acquiring inertial navigation data comprising attitude time information and attitude information in a second frequency acquisition mode through an inertial navigation system INS; acquiring the LiDAR data containing coordinate time information and coordinate information in a third frequency acquisition mode through LiDAR equipment;
wherein the first frequency < the second frequency < the third frequency.
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